scholarly journals A Modified Artificial Bee Colony for the Non-Smooth Dynamic Economic/Environmental Dispatch

2018 ◽  
Vol 8 (5) ◽  
pp. 3321-3328 ◽  
Author(s):  
Ι. Marouani ◽  
A. Boudjemline ◽  
T. Guesmi ◽  
H. H. Abdallah
Keyword(s):  
Artificial Bee Colony ◽  
Random Search ◽  
Line Planning ◽  
Bee Colony ◽  
Search Mechanism ◽  
Loading Effects ◽  
On Line ◽  
Explosion Method ◽  
Metaheuristic Techniques ◽  
Emission Dispatch

This paper presents an improved artificial bee colony (ABC) technique for solving the dynamic economic emission dispatch (DEED) problem. Ramp rate limits, valve-point loading effects and prohibited operating zones (POZs) have been considered. The proposed technique integrates the grenade explosion method and Cauchy operator in the original ABC algorithm, to avoid random search mechanism. However, the DEED is a multi-objective optimization problem with two conflicting criteria which need to be minimized simultaneously. Thus, it is recommended to provide the best solution for the decision-makers. Shannon’s entropy-based method is used for the first time within the context of the on-line planning of generator outputs to extract the best compromise solution among the Pareto set. The robustness of the proposed technique is verified on six-unit and ten-unit system tests. Results proved that the proposed algorithm gives better optimum solutions in comparison with more than ten metaheuristic techniques.

A quasi-affine transformation artificial bee colony algorithm for global optimization

2021 ◽  
pp. 1-18
Author(s):  
Baohua Zhao ◽  
Tien-Wen Sung ◽  
Xin Zhang
Keyword(s):  
Affine Transformation ◽  
Artificial Bee Colony ◽  
Triangular Matrix ◽  
Convergence Speed ◽  
Test Set ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Search Mechanism ◽  
Multiple Dimensions ◽  
Collaborative Search

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.

scholarly journals Combined Economic and Emission Dispatch Solution using Artificial Bee Colony Algorithm with Fuzzy Approach

2018 ◽  
Vol 7 (3.15) ◽  
pp. 46
Author(s):  
M N. Abdullah ◽  
G Y. Sim ◽  
A Azmi ◽  
S H. Shamsudin
Keyword(s):  
Power Generation ◽  
Power System ◽  
Artificial Bee Colony ◽  
Compromise Solution ◽  
Fuzzy Approach ◽  
Abc Algorithm ◽  
Unit System ◽  
Bee Colony ◽  
The Cost ◽  
Emission Dispatch

The cost and emission minimization in power system operation become important issue in power dispatch due to increase of environmental pollution and fossil fuel price. Therefore, combined economic and emission dispatch (CEED) must be considered in generation scheduling in order to provide balanced solution for optimal cost and emissions level of power generation. In this paper, an Artificial Bee Colony (ABC) algorithm with Fuzzy best compromise solution is proposed to determine the optimal cost and emission level by converting the multi-objective (cost and emission) into single objective problem using weighted sum method approach. The best compromise solution among Pareto front solution was determined by fuzzy approach. The effectiveness of ABC algorithm has been validated in terms of the best solution, convergence behaviour and consistency for power system benchmark such as IEEE 30-bus 6-unit system and 10-unit system. The comparison study shows that ABC algorithm capable to obtain a better performance of minimizing the cost and emission level in power generation.  

Artificial Bee Colony Algorithm for An Optimal Solution for Combined Economic and Emission Dispatch Problem

2016 ◽  
Vol 5 (3) ◽  
pp. 111
Author(s):  
Sandeep Bhongade ◽  
Sourabh Agarwal
Keyword(s):  
Artificial Bee Colony ◽  
Thermal Power ◽  
Optimal Solution ◽  
Green Energy ◽  
Oxides Of Nitrogen ◽  
Fuel Cost ◽  
Geothermal Heat ◽  
Power Stations ◽  
Bee Colony ◽  
Emission Dispatch

In India Electrical Energy is generated mainly Coal based Thermal Power stations and hydro Electric Power Stations. The main aim of power generating company is to provide good quality and reliable power to consumers at minimum cost. The problem of Combined Economic and Emission Dispatch  deals with the minimization of both fuel cost and emission of pollutants such as oxides of Nitrogen and Oxides of Sulphur. In our power system the emission is major problem created that’s why in now a days we move from green energy source or renewable energy such as Sunlight, Wind, Tides, Wave, and Geothermal Heat Energy. The Emission constrained Economic Dispatch problem treats the emission limit as an additional constraint and optimizes the fuel cost. In this paper we optimizes the Combined Economic and Emission Dispatch problem by using two different optimization method such as Artificial Bee Colony (ABC) and Genetic Algorithm (GA).The proposed ABC Algorithm has been successfully implemented is to IEEE 30 bus and Indian Utility sixty two Bus System The simulation result are compare and found the effective algorithm for Combined Economic and Emission Dispatch problem.

Research on Cellular Artificial Bee Colony Algorithm and its Computational Experiments

2013 ◽  
Vol 284-287 ◽  
pp. 3168-3172
Author(s):  
Ren Bin Xiao ◽  
Ying Cong Wang
Keyword(s):  
Population Structure ◽  
Cellular Automata ◽  
Selection Pressure ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Exploration And Exploitation ◽  
Fixed Position ◽  
Bee Colony ◽  
Search Mechanism ◽  
The Relationship

It is the research hotspot for evolutionary algorithms to solve the contradiction between exploration and exploitation. Cellular artificial bee colony (CABC) algorithm is proposed by combining cellular automata with artificial bee colony algorithm from the perspective of the neighborhood in this paper. Each bee in the population structure defined in CABC has a fixed position and can only interact with bees in its neighborhood. The overlap between neighborhoods of different bees may make a bee an employed bee in one neighborhood and an onlooker bee in another neighborhood and vice versa, which increases the diversity of the population. The neighborhood and evolutionary rule help to control the selection pressure effectively, and the improved search mechanism in artificial bee colony algorithm is proposed to enhance the local search ability. The experimental results tested on four benchmark functions show that CABC can further balance the relationship between exploration and exploitation when compared with three ABC-based algorithms.

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